DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.334394
A new method for estimating displacements in computer imagery through cluster matching is presented. Without reliance on any object model, the algorithm clusters two successive frames of an image sequence based on position and intensity. After clustering, displacement estimates are obtained by matching the cluster centers between the two frames using cluster features such as position, intensity, shape and average gray-scale difference. The performance of the algorithm was compared to that of a gradient method and a block matching method. The cluster matching approach showed the best performance over a broad range of motion, illumination change and object deformation. [1] J. K. Aggarwal and N. Nandhakumar, "On the computation of motion from sequences of images--A review,"Proc. IEEE, vol. 76, no. 8, pp. 917-935, 1988.
Index Terms:
motion estimation; image sequences; motion estimation; cluster matching; displacements estimation; computer imagery; image sequence; clustering; average gray-scale difference; gradient method; block matching method
Citation:
D.P. Kottke, Y. Sun, "Motion Estimation Via Cluster Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 11, pp. 1128-1132, Nov. 1994, doi:10.1109/34.334394 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||